From Learning Models of Natural Image Patches to Whole Image Restoration Daniel Zoran Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Yair Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Presented by Eric Wang Duke University 6/18/2012
14
Embed
From Learning Models of Natural Image Patches to Whole Image Restoration
From Learning Models of Natural Image Patches to Whole Image Restoration. Daniel Zoran Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Yair Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Presented by Eric Wang - PowerPoint PPT Presentation
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
From Learning Models of Natural Image Patches to Whole Image Restoration
Daniel ZoranInterdisciplinary Center for Neural Computation
Hebrew University of Jerusalem
Yair WeissSchool of Computer Science and Engineering
Hebrew University of Jerusalem
Presented by Eric WangDuke University
6/18/2012
Introduction• Patch based learning on images has significant computational
advantages over learning dictionaries over the entire image.
• A primary concern of this paper is the affect the choice of dictionary priors have on the performance of the model.
• This paper addresses 3 main questions– (1) Do priors that give high likelihoods yield better patch restoration
performance?– (2) Do priors that give high likelihoods yield better whole-image
restoration performance? – (3) Can we learn better priors?
Motivation and Patch Restoration• Answer to question (1): Priors with higher likelihoods will yield
improved per-patch denoising performance, as shown with several popular priors
PSNR of restoration on patches vs. dictionary (prior) likelihoods. Trained on 50,000 8x8 patches of natural images of faces. Tested on unseen patches.
From Patches to Whole Image Restoration• Good patch-based image restoration does not guarantee high
quality image restoration, many reconstruction methods can generate significant artifacts.
Expected Patch Log-Likelihood • Choosing random patches can provide a solution to minimize
artifacting, but has the issue that most random patches will have low likelihood to a given dictionary.
• This paper presents an optimization algorithm that maximizes expected patch log-likelihood (EPLL) with the constraint that the reconstructed image be close to the original image.
• The EPLL under prior p is defined as
• Where is a mask that extracts the ith random patch from image x.
Cost Function• Let be the corruption model on the image where x
is a vectorized image, A defines the corruption model and y is the vectorized noisy observation.
• The cost function to minimize is then
• Direct optimization of this cost function is intractable, so an alternative method called half quadratic splitting is used, where are a set of per-patch auxiliary variables.
The Corruption Model • The choice of the matrix A is determined by the application.
• For denoising, A is an identity matrix, and is the noise precision
• For deblurring, A is a convolution matrix with a known kernel
• For inpainting, A is a diagonal matrix with zeros for the missing elements.
Optimization• The EPLL optimization involves two steps: (1) solving for
given
• And (2) solving for given , which is dependent on the dictionary and involves solving a MAP estimate of the most likely dictionary element for a particular patch.
• is either set by hand or set to where is the estimated noise standard deviation in .
Image Restoration• Answer to question (2): It is shown that priors with higher